{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "vitaldb_open_dataset.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyO7H6x8Udx7bp0FMn66HZdm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vitaldb/examples/blob/master/vitaldb_open_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yy0WHpW3X9oX"
      },
      "source": [
        "# Using VitalDB open dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "90F2b98INgeC"
      },
      "source": [
        "## Preparation\n",
        "For using the VitalDB open dataset, first we need to load 3 endpoints using the pandas library."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ijs7lU5cNjfD"
      },
      "source": [
        "import pandas as pd\n",
        "df_cases = pd.read_csv(\"https://api.vitaldb.net/cases\")  # clinical information\n",
        "df_trks = pd.read_csv(\"https://api.vitaldb.net/trks\")  # track list\n",
        "df_labs = pd.read_csv('https://api.vitaldb.net/labs')  # laboratory results"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F7TntrRbfdt2"
      },
      "source": [
        "## Clinical informations\n",
        "Let's check the cases and variables in the clinical information table.\n",
        "\n",
        "Complete list of parameters and their description is available at https://vitaldb.net/docs/?documentId=13qqajnNZzkN7NZ9aXnaQ-47NWy7kx-a6gbrcEsi-gak#h.y1yyuwuwpa9c"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rj6zxZtnX805",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "cd422192-a7e4-42ae-ca05-52394e9a32a1"
      },
      "source": [
        "df_cases"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      caseid  subjectid  casestart  caseend  anestart   aneend  opstart  \\\n",
              "0          1       5955          0    11542      -552  10848.0     1668   \n",
              "1          2       2487          0    15741     -1039  14921.0     1721   \n",
              "2          3       2861          0     4394      -590   4210.0     1090   \n",
              "3          4       1903          0    20990      -778  20222.0     2522   \n",
              "4          5       4416          0    21531     -1009  22391.0     2591   \n",
              "...      ...        ...        ...      ...       ...      ...      ...   \n",
              "6383    6384       5583          0    15248      -260  15640.0     2140   \n",
              "6384    6385       2278          0    20643      -544  20996.0     2396   \n",
              "6385    6386       4045          0    19451      -667  19133.0     3533   \n",
              "6386    6387       5230          0    12025      -550  12830.0     1730   \n",
              "6387    6388       1306          0    10249       -79  10121.0     2321   \n",
              "\n",
              "      opend     adm      dis  ...  intraop_colloid  intraop_ppf  intraop_mdz  \\\n",
              "0     10368 -236220   627780  ...                0          120          0.0   \n",
              "1     14621 -221160  1506840  ...                0          150          0.0   \n",
              "2      3010 -218640    40560  ...                0            0          0.0   \n",
              "3     17822 -201120   576480  ...                0           80          0.0   \n",
              "4     20291  -67560  3734040  ...                0            0          0.0   \n",
              "...     ...     ...      ...  ...              ...          ...          ...   \n",
              "6383  14140 -215340   648660  ...                0          150          0.0   \n",
              "6384  19496 -225600  1675200  ...                0          100          0.0   \n",
              "6385  18233 -200460   836340  ...                0           70          0.0   \n",
              "6386  11030 -227760   377040  ...                0          120          0.0   \n",
              "6387   9221 -312060   379140  ...              500          120          0.0   \n",
              "\n",
              "     intraop_ftn  intraop_rocu  intraop_vecu  intraop_eph  intraop_phe  \\\n",
              "0            100            70             0           10            0   \n",
              "1              0           100             0           20            0   \n",
              "2              0            50             0            0            0   \n",
              "3            100           100             0           50            0   \n",
              "4              0           160             0           10          900   \n",
              "...          ...           ...           ...          ...          ...   \n",
              "6383           0            90             0           20            0   \n",
              "6384           0           100             0           25           30   \n",
              "6385           0           130             0           10            0   \n",
              "6386           0            50             0            0            0   \n",
              "6387           0            90             0           20            0   \n",
              "\n",
              "      intraop_epi intraop_ca  \n",
              "0               0          0  \n",
              "1               0          0  \n",
              "2               0          0  \n",
              "3               0          0  \n",
              "4               0       2100  \n",
              "...           ...        ...  \n",
              "6383            0          0  \n",
              "6384            0        300  \n",
              "6385            0          0  \n",
              "6386            0          0  \n",
              "6387            0          0  \n",
              "\n",
              "[6388 rows x 74 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ce01f43a-9835-4d1f-a8ef-019f7b88120d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>caseid</th>\n",
              "      <th>subjectid</th>\n",
              "      <th>casestart</th>\n",
              "      <th>caseend</th>\n",
              "      <th>anestart</th>\n",
              "      <th>aneend</th>\n",
              "      <th>opstart</th>\n",
              "      <th>opend</th>\n",
              "      <th>adm</th>\n",
              "      <th>dis</th>\n",
              "      <th>...</th>\n",
              "      <th>intraop_colloid</th>\n",
              "      <th>intraop_ppf</th>\n",
              "      <th>intraop_mdz</th>\n",
              "      <th>intraop_ftn</th>\n",
              "      <th>intraop_rocu</th>\n",
              "      <th>intraop_vecu</th>\n",
              "      <th>intraop_eph</th>\n",
              "      <th>intraop_phe</th>\n",
              "      <th>intraop_epi</th>\n",
              "      <th>intraop_ca</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5955</td>\n",
              "      <td>0</td>\n",
              "      <td>11542</td>\n",
              "      <td>-552</td>\n",
              "      <td>10848.0</td>\n",
              "      <td>1668</td>\n",
              "      <td>10368</td>\n",
              "      <td>-236220</td>\n",
              "      <td>627780</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100</td>\n",
              "      <td>70</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2487</td>\n",
              "      <td>0</td>\n",
              "      <td>15741</td>\n",
              "      <td>-1039</td>\n",
              "      <td>14921.0</td>\n",
              "      <td>1721</td>\n",
              "      <td>14621</td>\n",
              "      <td>-221160</td>\n",
              "      <td>1506840</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2861</td>\n",
              "      <td>0</td>\n",
              "      <td>4394</td>\n",
              "      <td>-590</td>\n",
              "      <td>4210.0</td>\n",
              "      <td>1090</td>\n",
              "      <td>3010</td>\n",
              "      <td>-218640</td>\n",
              "      <td>40560</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1903</td>\n",
              "      <td>0</td>\n",
              "      <td>20990</td>\n",
              "      <td>-778</td>\n",
              "      <td>20222.0</td>\n",
              "      <td>2522</td>\n",
              "      <td>17822</td>\n",
              "      <td>-201120</td>\n",
              "      <td>576480</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>4416</td>\n",
              "      <td>0</td>\n",
              "      <td>21531</td>\n",
              "      <td>-1009</td>\n",
              "      <td>22391.0</td>\n",
              "      <td>2591</td>\n",
              "      <td>20291</td>\n",
              "      <td>-67560</td>\n",
              "      <td>3734040</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>160</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>900</td>\n",
              "      <td>0</td>\n",
              "      <td>2100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6383</th>\n",
              "      <td>6384</td>\n",
              "      <td>5583</td>\n",
              "      <td>0</td>\n",
              "      <td>15248</td>\n",
              "      <td>-260</td>\n",
              "      <td>15640.0</td>\n",
              "      <td>2140</td>\n",
              "      <td>14140</td>\n",
              "      <td>-215340</td>\n",
              "      <td>648660</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6384</th>\n",
              "      <td>6385</td>\n",
              "      <td>2278</td>\n",
              "      <td>0</td>\n",
              "      <td>20643</td>\n",
              "      <td>-544</td>\n",
              "      <td>20996.0</td>\n",
              "      <td>2396</td>\n",
              "      <td>19496</td>\n",
              "      <td>-225600</td>\n",
              "      <td>1675200</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6385</th>\n",
              "      <td>6386</td>\n",
              "      <td>4045</td>\n",
              "      <td>0</td>\n",
              "      <td>19451</td>\n",
              "      <td>-667</td>\n",
              "      <td>19133.0</td>\n",
              "      <td>3533</td>\n",
              "      <td>18233</td>\n",
              "      <td>-200460</td>\n",
              "      <td>836340</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>70</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6386</th>\n",
              "      <td>6387</td>\n",
              "      <td>5230</td>\n",
              "      <td>0</td>\n",
              "      <td>12025</td>\n",
              "      <td>-550</td>\n",
              "      <td>12830.0</td>\n",
              "      <td>1730</td>\n",
              "      <td>11030</td>\n",
              "      <td>-227760</td>\n",
              "      <td>377040</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6387</th>\n",
              "      <td>6388</td>\n",
              "      <td>1306</td>\n",
              "      <td>0</td>\n",
              "      <td>10249</td>\n",
              "      <td>-79</td>\n",
              "      <td>10121.0</td>\n",
              "      <td>2321</td>\n",
              "      <td>9221</td>\n",
              "      <td>-312060</td>\n",
              "      <td>379140</td>\n",
              "      <td>...</td>\n",
              "      <td>500</td>\n",
              "      <td>120</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6388 rows × 74 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce01f43a-9835-4d1f-a8ef-019f7b88120d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ce01f43a-9835-4d1f-a8ef-019f7b88120d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ce01f43a-9835-4d1f-a8ef-019f7b88120d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i58yOfC6xJ5q"
      },
      "source": [
        "Then, check the missing rate in the clinical information."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GxB1uGp-sAH2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "9c506852-445c-4d8d-82b7-bba333dd5094"
      },
      "source": [
        "pd.DataFrame((df_cases.isnull().mean() * 100).sort_values(ascending=False), columns=['missing rate'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            missing rate\n",
              "cline2         99.060739\n",
              "lmasize        98.403256\n",
              "aline2         98.356293\n",
              "preop_be       91.671885\n",
              "preop_sao2     91.656230\n",
              "...                  ...\n",
              "ane_type        0.000000\n",
              "preop_htn       0.000000\n",
              "preop_dm        0.000000\n",
              "preop_ecg       0.000000\n",
              "intraop_ca      0.000000\n",
              "\n",
              "[74 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-818697f6-da41-4a57-ae71-ea07788b4341\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>missing rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>cline2</th>\n",
              "      <td>99.060739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lmasize</th>\n",
              "      <td>98.403256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>aline2</th>\n",
              "      <td>98.356293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>preop_be</th>\n",
              "      <td>91.671885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>preop_sao2</th>\n",
              "      <td>91.656230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ane_type</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>preop_htn</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>preop_dm</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>preop_ecg</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>intraop_ca</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>74 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-818697f6-da41-4a57-ae71-ea07788b4341')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-818697f6-da41-4a57-ae71-ea07788b4341 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-818697f6-da41-4a57-ae71-ea07788b4341');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-osZmtfnOGTo"
      },
      "source": [
        "## Track List\n",
        "The Track list table has a track name (`tname`) and track ID (`tid`) for each case.\n",
        "\n",
        "Complete list of tracks and their description is available at https://vitaldb.net/docs/?documentId=13qqajnNZzkN7NZ9aXnaQ-47NWy7kx-a6gbrcEsi-gak#h.d0wofkno1fxp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "PyDXgmgAOITn",
        "outputId": "785099be-d7c3-417f-886c-d4f39ed82b33"
      },
      "source": [
        "df_trks"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        caseid                  tname  \\\n",
              "0            1                BIS/BIS   \n",
              "1            1           BIS/EEG1_WAV   \n",
              "2            1           BIS/EEG2_WAV   \n",
              "3            1                BIS/EMG   \n",
              "4            1                BIS/SEF   \n",
              "...        ...                    ...   \n",
              "486444    6388     Solar8000/VENT_PIP   \n",
              "486445    6388   Solar8000/VENT_PPLAT   \n",
              "486446    6388      Solar8000/VENT_RR   \n",
              "486447    6388  Solar8000/VENT_SET_TV   \n",
              "486448    6388      Solar8000/VENT_TV   \n",
              "\n",
              "                                             tid  \n",
              "0       fd869e25ba82a66cc95b38ed47110bf4f14bb368  \n",
              "1       0aa685df768489a18a5e9f53af0d83bf60890c73  \n",
              "2       ad13b2c39b19193c8ae4a2de4f8315f18d61a57e  \n",
              "3       2525603efe18d982764dbca457affe7a45e766a9  \n",
              "4       1c91aec859304840dec75acf4a35da78be0e8ef0  \n",
              "...                                          ...  \n",
              "486444  2d63adbc7e2653f14348e219816673cde3358cf6  \n",
              "486445  6f6604255858ddc8f6a01b9f4774b0d43105f6da  \n",
              "486446  f34f3fae7fd963355c1c7060e1e876d20fa87536  \n",
              "486447  4a4a55b8aebf9c76a4a76f62a7c1ec6fcb80e618  \n",
              "486448  77453bd3c4bf24ce13e577781a51929281f3b7f2  \n",
              "\n",
              "[486449 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-723a802d-7f87-471c-88f9-878566b53ee4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>caseid</th>\n",
              "      <th>tname</th>\n",
              "      <th>tid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>BIS/BIS</td>\n",
              "      <td>fd869e25ba82a66cc95b38ed47110bf4f14bb368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>BIS/EEG1_WAV</td>\n",
              "      <td>0aa685df768489a18a5e9f53af0d83bf60890c73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>BIS/EEG2_WAV</td>\n",
              "      <td>ad13b2c39b19193c8ae4a2de4f8315f18d61a57e</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>BIS/EMG</td>\n",
              "      <td>2525603efe18d982764dbca457affe7a45e766a9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>BIS/SEF</td>\n",
              "      <td>1c91aec859304840dec75acf4a35da78be0e8ef0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>486444</th>\n",
              "      <td>6388</td>\n",
              "      <td>Solar8000/VENT_PIP</td>\n",
              "      <td>2d63adbc7e2653f14348e219816673cde3358cf6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>486445</th>\n",
              "      <td>6388</td>\n",
              "      <td>Solar8000/VENT_PPLAT</td>\n",
              "      <td>6f6604255858ddc8f6a01b9f4774b0d43105f6da</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>486446</th>\n",
              "      <td>6388</td>\n",
              "      <td>Solar8000/VENT_RR</td>\n",
              "      <td>f34f3fae7fd963355c1c7060e1e876d20fa87536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>486447</th>\n",
              "      <td>6388</td>\n",
              "      <td>Solar8000/VENT_SET_TV</td>\n",
              "      <td>4a4a55b8aebf9c76a4a76f62a7c1ec6fcb80e618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>486448</th>\n",
              "      <td>6388</td>\n",
              "      <td>Solar8000/VENT_TV</td>\n",
              "      <td>77453bd3c4bf24ce13e577781a51929281f3b7f2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>486449 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-723a802d-7f87-471c-88f9-878566b53ee4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-723a802d-7f87-471c-88f9-878566b53ee4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-723a802d-7f87-471c-88f9-878566b53ee4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_zLg_Apsxxw0"
      },
      "source": [
        "Let's check the missing rate of the track table."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ot04Yaydx5SP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "4d6bf3ac-7164-4ffb-ba60-fa25f5f64745"
      },
      "source": [
        "missing_rates = []\n",
        "for tname in sorted(df_trks['tname'].unique()):\n",
        "    missing_rates.append({'track': tname, 'missing rate': 100 - (df_trks['tname'] == tname).sum() / len(df_cases) * 100})\n",
        "pd.DataFrame(missing_rates).sort_values(by='missing rate')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    track  missing rate\n",
              "146          Solar8000/HR      0.015654\n",
              "155  Solar8000/PLETH_SPO2      0.031309\n",
              "154    Solar8000/PLETH_HR      0.031309\n",
              "91             Primus/CO2      0.407013\n",
              "109      Primus/PAMB_MBAR      0.422668\n",
              "..                    ...           ...\n",
              "39     Orchestra/AMD_RATE     99.984346\n",
              "40      Orchestra/AMD_VOL     99.984346\n",
              "59     Orchestra/NPS_RATE     99.984346\n",
              "89      Orchestra/VEC_VOL     99.984346\n",
              "88     Orchestra/VEC_RATE     99.984346\n",
              "\n",
              "[196 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-58be0433-a1ca-48da-aa24-88395aefa0b9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>track</th>\n",
              "      <th>missing rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>Solar8000/HR</td>\n",
              "      <td>0.015654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>Solar8000/PLETH_SPO2</td>\n",
              "      <td>0.031309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>Solar8000/PLETH_HR</td>\n",
              "      <td>0.031309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Primus/CO2</td>\n",
              "      <td>0.407013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>Primus/PAMB_MBAR</td>\n",
              "      <td>0.422668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Orchestra/AMD_RATE</td>\n",
              "      <td>99.984346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Orchestra/AMD_VOL</td>\n",
              "      <td>99.984346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Orchestra/NPS_RATE</td>\n",
              "      <td>99.984346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Orchestra/VEC_VOL</td>\n",
              "      <td>99.984346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>Orchestra/VEC_RATE</td>\n",
              "      <td>99.984346</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>196 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-58be0433-a1ca-48da-aa24-88395aefa0b9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-58be0433-a1ca-48da-aa24-88395aefa0b9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-58be0433-a1ca-48da-aa24-88395aefa0b9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VXTsgLFnzngc"
      },
      "source": [
        "## Laboratory results\n",
        "Complete list of tracks and their description is available at https://vitaldb.net/docs/?documentId=13qqajnNZzkN7NZ9aXnaQ-47NWy7kx-a6gbrcEsi-gak#h.qh5znywc24u2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "etZ6RGBAzsRc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "b85a5b81-d545-4985-93cd-60342520c3d6"
      },
      "source": [
        "print('{} lab types'.format(len(df_labs['name'].unique())))\n",
        "df_labs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "34 lab types\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        caseid      dt  name  result\n",
              "0            1  594470   alb    2.90\n",
              "1            1  399575   alb    3.20\n",
              "2            1   12614   alb    3.40\n",
              "3            1  137855   alb    3.60\n",
              "4            1  399575   alt   12.00\n",
              "...        ...     ...   ...     ...\n",
              "928443    6388    3503  sao2  100.00\n",
              "928444    6388  408770   wbc    3.28\n",
              "928445    6388  -32848   wbc    6.27\n",
              "928446    6388 -249820   wbc    7.66\n",
              "928447    6388   62645   wbc   11.34\n",
              "\n",
              "[928448 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b652356e-a6a0-47c0-b2c0-d1f3c025dcdf\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>caseid</th>\n",
              "      <th>dt</th>\n",
              "      <th>name</th>\n",
              "      <th>result</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>594470</td>\n",
              "      <td>alb</td>\n",
              "      <td>2.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>399575</td>\n",
              "      <td>alb</td>\n",
              "      <td>3.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>12614</td>\n",
              "      <td>alb</td>\n",
              "      <td>3.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>137855</td>\n",
              "      <td>alb</td>\n",
              "      <td>3.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>399575</td>\n",
              "      <td>alt</td>\n",
              "      <td>12.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>928443</th>\n",
              "      <td>6388</td>\n",
              "      <td>3503</td>\n",
              "      <td>sao2</td>\n",
              "      <td>100.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>928444</th>\n",
              "      <td>6388</td>\n",
              "      <td>408770</td>\n",
              "      <td>wbc</td>\n",
              "      <td>3.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>928445</th>\n",
              "      <td>6388</td>\n",
              "      <td>-32848</td>\n",
              "      <td>wbc</td>\n",
              "      <td>6.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>928446</th>\n",
              "      <td>6388</td>\n",
              "      <td>-249820</td>\n",
              "      <td>wbc</td>\n",
              "      <td>7.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>928447</th>\n",
              "      <td>6388</td>\n",
              "      <td>62645</td>\n",
              "      <td>wbc</td>\n",
              "      <td>11.34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>928448 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b652356e-a6a0-47c0-b2c0-d1f3c025dcdf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b652356e-a6a0-47c0-b2c0-d1f3c025dcdf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b652356e-a6a0-47c0-b2c0-d1f3c025dcdf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dRJWr7f9Mhff"
      },
      "source": [
        "## Inclusion & Exclusion Criteria\n",
        "Research using VitalDB open dataset usually starts with finding cases that meet the inclusion and exclusion criteria. The code below is an example of doing this."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opEvfWTypKmc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b5defff-338d-4e60-d1cb-e9835f971b0d"
      },
      "source": [
        "caseids = list(\n",
        "    set(df_trks.loc[df_trks['tname'] == 'Solar8000/ART_MBP', 'caseid']) & \n",
        "    set(df_cases.loc[df_cases['age'] > 18, 'caseid']) & \n",
        "    set(df_cases.loc[~df_cases['opname'].str.lower().str.contains(\"transplant\"), 'caseid'])\n",
        ")\n",
        "print(f'{len(caseids)} cases found')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3452 cases found\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0DUoONVTt4K"
      },
      "source": [
        "## Read track samples using `load_case` function\n",
        "The next step is to read the actual samples in a specific track. Using the `vitaldb` library is the simplest way."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9APT2QjNVVRV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c762e2a-80fe-4cde-ff08-01b1bdb6a306"
      },
      "source": [
        "!pip install vitaldb"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting vitaldb\n",
            "  Downloading vitaldb-1.2.5-py3-none-any.whl (52 kB)\n",
            "\u001b[K     |████████████████████████████████| 52 kB 783 kB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from vitaldb) (2.23.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from vitaldb) (1.21.6)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from vitaldb) (1.3.5)\n",
            "Collecting wfdb\n",
            "  Downloading wfdb-4.0.0-py3-none-any.whl (161 kB)\n",
            "\u001b[K     |████████████████████████████████| 161 kB 29.2 MB/s \n",
            "\u001b[?25hCollecting s3fs\n",
            "  Downloading s3fs-2022.8.2-py3-none-any.whl (27 kB)\n",
            "Requirement already satisfied: pyarrow in /usr/local/lib/python3.7/dist-packages (from vitaldb) (6.0.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->vitaldb) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->vitaldb) (2022.2.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->vitaldb) (1.15.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->vitaldb) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->vitaldb) (2022.6.15)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->vitaldb) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->vitaldb) (1.24.3)\n",
            "Requirement already satisfied: fsspec==2022.8.2 in /usr/local/lib/python3.7/dist-packages (from s3fs->vitaldb) (2022.8.2)\n",
            "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.7/dist-packages (from s3fs->vitaldb) (3.8.1)\n",
            "Collecting aiobotocore~=2.4.0\n",
            "  Downloading aiobotocore-2.4.0-py3-none-any.whl (65 kB)\n",
            "\u001b[K     |████████████████████████████████| 65 kB 4.1 MB/s \n",
            "\u001b[?25hCollecting botocore<1.27.60,>=1.27.59\n",
            "  Downloading botocore-1.27.59-py3-none-any.whl (9.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 9.1 MB 45.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: wrapt>=1.10.10 in /usr/local/lib/python3.7/dist-packages (from aiobotocore~=2.4.0->s3fs->vitaldb) (1.14.1)\n",
            "Collecting aioitertools>=0.5.1\n",
            "  Downloading aioitertools-0.10.0-py3-none-any.whl (23 kB)\n",
            "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs->vitaldb) (2.1.1)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs->vitaldb) (4.0.2)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs->vitaldb) (4.1.1)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs->vitaldb) (1.2.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs->vitaldb) (6.0.2)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs->vitaldb) (22.1.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs->vitaldb) (1.3.1)\n",
            "Requirement already satisfied: asynctest==0.13.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs->vitaldb) (0.13.0)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs->vitaldb) (1.8.1)\n",
            "Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
            "  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
            "\u001b[K     |████████████████████████████████| 127 kB 51.2 MB/s \n",
            "\u001b[?25hCollecting jmespath<2.0.0,>=0.7.1\n",
            "  Downloading jmespath-1.0.1-py3-none-any.whl (20 kB)\n",
            "Requirement already satisfied: SoundFile<0.12.0,>=0.10.0 in /usr/local/lib/python3.7/dist-packages (from wfdb->vitaldb) (0.10.3.post1)\n",
            "Requirement already satisfied: matplotlib<4.0.0,>=3.2.2 in /usr/local/lib/python3.7/dist-packages (from wfdb->vitaldb) (3.2.2)\n",
            "Requirement already satisfied: scipy<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from wfdb->vitaldb) (1.7.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb->vitaldb) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb->vitaldb) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb->vitaldb) (1.4.4)\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.7/dist-packages (from SoundFile<0.12.0,>=0.10.0->wfdb->vitaldb) (1.15.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.0->SoundFile<0.12.0,>=0.10.0->wfdb->vitaldb) (2.21)\n",
            "Installing collected packages: urllib3, jmespath, botocore, aioitertools, aiobotocore, wfdb, s3fs, vitaldb\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "Successfully installed aiobotocore-2.4.0 aioitertools-0.10.0 botocore-1.27.59 jmespath-1.0.1 s3fs-2022.8.2 urllib3-1.25.11 vitaldb-1.2.5 wfdb-4.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "XMfKAkuNUPZc",
        "outputId": "601206af-a956-4a07-8b74-6b732657a776"
      },
      "source": [
        "import vitaldb\n",
        "vals = vitaldb.load_case(1, ['SNUADC/ART','Solar8000/ART_SBP'], 1/100)\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(20, 5))\n",
        "plt.plot(vals[:, 0], color='red')\n",
        "plt.plot(vals[:, 1], color='orange', marker='x')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "zSv9M0h7ZZuE",
        "outputId": "62f468c9-a9ed-4c99-e440-c802c06a5cbe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[         nan          nan]\n",
            " [         nan          nan]\n",
            " [         nan          nan]\n",
            " ...\n",
            " [  0.148893   -32.50870132]\n",
            " [ -0.32508701  19.82659912]\n",
            " [         nan          nan]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "vals = vitaldb.load_case(caseids[0], ['ECG_II','ART'], 1/100)\n",
        "print(vals)\n",
        "\n",
        "ecg = vals[:,0]\n",
        "art = vals[:,1]\n",
        "\n",
        "# plot\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(20,10))\n",
        "plt.subplot(211)\n",
        "plt.plot(ecg[110000:111000], color='g')\n",
        "plt.subplot(212)\n",
        "plt.plot(art[110000:111000], color='r')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bxrktnXaXPQ7"
      },
      "source": [
        "## Read track samples using `VitalFile` class"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ETIrPMJb4DQ"
      },
      "source": [
        "Same as `load_case` in the functional API, you can download the tracks from the VitalDB open dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "czvq-HfaaXRa",
        "outputId": "037e012e-4283-4163-8756-7862c7738e24"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[nan nan  0.]\n",
            " [nan nan  0.]\n",
            " [ 0.  0.  0.]\n",
            " ...\n",
            " [ 0.  0.  0.]\n",
            " [ 0.  0.  0.]\n",
            " [ 0.  0. nan]]\n"
          ]
        }
      ],
      "source": [
        "# caseid of tiva cases\n",
        "caseid = list(vitaldb.caseids_tiva)[0]\n",
        "track_names = ['PPF20_RATE', 'RFTN20_RATE', 'BIS']\n",
        "vf = vitaldb.VitalFile(caseid, track_names)\n",
        "\n",
        "vals = vf.to_numpy(track_names, 1)\n",
        "print(vals)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Add event track"
      ],
      "metadata": {
        "id": "ureFcWj7hz6W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# find the range of valid values (rate > 1 mL/h)\n",
        "valid_ppf_idx = np.where(vals[:,0] > 1)[0]\n",
        "valid_rft_idx = np.where(vals[:,1] > 1)[0]\n",
        "\n",
        "first_valid_idx = min(valid_ppf_idx[0], valid_rft_idx[0])\n",
        "last_valid_idx = max(valid_ppf_idx[-1], valid_rft_idx[-1])\n",
        "\n",
        "# convert sample index to date time\n",
        "first_valid_dt = vf.dtstart + first_valid_idx\n",
        "last_valid_dt = vf.dtstart + last_valid_idx\n",
        "\n",
        "# make event track\n",
        "event_recs = []\n",
        "event_recs.append({'dt': first_valid_dt, 'val': 'Infusion start'})\n",
        "event_recs.append({'dt': last_valid_dt, 'val': 'Infusion end'})\n",
        "\n",
        "# add track and save\n",
        "vf.add_track('EVENT', event_recs, mindisp=0, maxdisp=10)\n",
        "vf.to_vital('labelled.vital')"
      ],
      "metadata": {
        "id": "8USVEIbUh8A6",
        "outputId": "651d3cd4-14bb-401d-a9a9-588016c25030",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wnvjcEG-kp3c"
      },
      "source": [
        "## More examples\n",
        "More examples using clinical information data in VitalDB are as below.\n",
        "\n",
        "\n",
        "* https://github.com/vitaldb/examples/blob/master/asa_mortality.ipynb\n",
        "* https://github.com/vitaldb/examples/blob/master/predict_mortality.ipynb\n",
        "* https://github.com/vitaldb/examples/blob/master/ppf_bis.ipynb"
      ]
    }
  ]
}